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Exploring Transfer Learning on Face Recognition of Dark Skinned, Low Quality and Low Resource Face Data

Record type:

paper
Creator:
Ali
Host:avatar
There is a big difference in the tone of color of skin between dark and light skinned people. Despite this fact, most face recognition tasks almost all classical state-of-the-art models are trained on datasets containing an overwhelming majority of light skinned face images. It is tedious to collect a huge amount of data for dark skinned faces and train a model from scratch. In this paper, we apply transfer learning on VGGFace to check how it works on recognising dark skinned mainly Ethiopian faces. The dataset is of low quality and low resource. Our experimental results show above 95\% accuracy which indicates that transfer learning in such settings works. 3 pages, 2 figures

Visit

arxiv.org

Tasks

computer visionimage classificationtransfer learning

Languages

Amharic

Tags

Computer Vision and Pattern RecognitionMachine Learning

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